Transcript lecture12_2010

Biological Networks
Can a biologist fix a radio?
Lazebnik, Cancer Cell, 2002
Building models from parts lists
Lazebnik, Cancer Cell, 2002
Computational tools are
needed to distill pathways
of interest from large
molecular interaction
databases
Jeong et al. Nature 411, 41 - 42 (2001)
Different types of Biological Networks
Network
Nodes
Links
Interaction
Protein Interaction
Metabolic
Transcriptional
Proteins
Metabolites
Transcription factor
Target genes
Physical Interaction
Enzymatic
conversion
Transcriptional
Interaction
Protein-Protein
Protein-Metabolite
Protein-DNA
A
A
A
A
B
B
B
B
Network Representation
regulates
gene A
gene B
regulatory interactions
(protein-DNA)
Protein B
functional complex
(protein-protein)
binds
protein A
Enzymatic
reaction Metabolite
Metabolite
B
A
node
edge
metabolic pathways
Network Analysis
Path
Hub
Clique
node
edge
Scale Free vs Random Networks
Small-world Network
• Every node can be reached from every other by
a small number of steps
Social networks, the Internet, and biological
networks all exhibit small-world network
characteristics
What can we learn from a
network?
Searching for critical positions in a network ?
Searching for critical positions in a network ?
High degree
Searching for critical positions in a network ?
High degree
High closeness
Searching for critical positions in a network ?
High degree
High closeness
High betweenness
Features of cellular Networks
Hubs are highly
connected nodes
• hubs tend not to interact directly with other hubs.
• Hubs tend to be “older” proteins
• Hubs are evolutionary conserved
In a scale free network more proteins are connected to the hubs
Albert et al. Science (2000) 406 378-382
In yeast, only ~20% of proteins are lethal when deleted
Lethal
Slow-growth
Non-lethal
Unknown
Jeong et al. Nature 411, 41 - 42 (2001)
Networks can help to predict
function
Mapping the phenotypic data to the network
•Systematic phenotyping
of 1615 gene knockout
strains in yeast
•Evaluation of growth of
each strain in the
presence of MMS (and
other DNA damaging
agents)
•Screening against a
network of 12,232 protein
interactions
Begley TJ, Mol Cancer Res. 2002
Mapping the phenotypic data to the network
Begley TJ, Mol Cancer Res. 2002
Mapping the phenotypic data to the network
Begley TJ, Mol Cancer Res. 2002
Networks can help to predict
function
Begley TJ, Mol Cancer Res. 2002.
Finding Local properties of
Biological Networks: Network Motifs
• Network motifs are recurrent
circuit elements.
• We can study a network by
looking at its parts (or
motifs)
• How many motifs are in the
network?
Adapted from :“An introduction to systems biology” by Uri Alon
Finding Local properties of
Biological Networks: Motifs
Finding Local properties of
Biological Networks: Motifs
Finding Local properties of
Biological Networks: Motifs
Finding Local properties of
Biological Networks: Motifs
Finding Local properties of
Biological Networks: Motifs
• What are these motifs?
• What biological relevance they
have?
Autoregulatory loop
• The probability of having
autoregulatory loops in a random
network is ~ 0 !!!!.
• Transcription networks: The
regulation of a gene by its own
product.
• Protein-Protein interaction network:
dimerization
Autoregulatory loop
What is the effect of Autoregulatory loops
on gene expression levels?
• Negative autoregulation
• Stable steady state
[protein]
[protein]
• Positive autoregulation
• Fast time-rise of protein
level
time
time
Three-node loops
There are 13 possible structures with 3 nodes

But in biological networks you can find only 2!
Feed forward loop
X
Y
Z
Feedback loop
X
Y
Z
Feedback loop
X
Y
Z
Course Summary
What did we learn
• Pairwise alignment –
Local and Global Alignments
When? How ?
Tools : for local blast2seq ,
for global best use MSA tools such as Clustal X, Muscle
What did we learn
• Multiple alignments (MSA)
When? How ?
MSA are needed as an input for many
different purposes: searching motifs,
phylogenetic analysis, protein and RNA
structure predictions, conservation of
specific nts/residues
Tools : Clustal X (for DNA and RNA), MUSCLE (for proteins)
Tools for phylogenetic trees: PHYLIP …
What did we learn
• Search a sequence against a database
When? How ?
- BLAST :Remember different option for BLAST!!!
(blastP blastN…. ), make sure to search the right
database!!!
DO NOT FORGET –You can change the scoring
matrices, gap penalty etc
- PSIBLAST
Searching for remote homologies
- PHIBLAST
Searching for a short pattern within a protein
What did we learn
• Motif search
When? How ?
- Searching for known motifs in a given
promoter (JASPAR)
-Searching for overabundance of unknown
regulatory motifs in a set of sequences ;
e.g promoters of genes which have similar
expression pattern (MEME)
Tools : MEME, logo,
Databases of motifs : JASPAR (Transcription Factors binding sites)
PRATT in PROSITE (searching for motifs in protein sequences)
What did we learn
• Protein Function Prediction
When? How ?
- Pfam (database to search for protein
motifs/domain (PfamA/PfamB)
- PROSITE
- Protein annotations in UNIPROT
(SwissProt/ Tremble)
What did we learn
• Protein Secondary Structure PredictionWhen? How ?
– Helix/Beta/Coil(PHDsec,PSIPRED).
– Predicts transmembrane helices
(PHDhtm,TMHMM).
– Solvent accessibility: important for the
prediction of ligand binding sites (PHDacc).
What did we learn
• Protein Tertiary Structure PredictionWhen? How ?
– First we must look at sequence identity to a
sequence with a known structure!!
– Homology modeling/Threading
– MODEBase- database of models
Remember : Low quality models can be miss
leading !!
Tools : SWISS-MODEL ,genTHREADER, MODEBase
What did we learn
• RNA Structure and Function PredictionWhen? How ?
– RNAfold – good for local interactions, several
predictions of low energy structures
– Alifold – adding information from MSA
– RFAM
– Specific database and search tools: tRNA,
microRNA …..
What did we learn
• Gene expression
When? How ?
– Many database of gene expression
GEO …
– Clustering analysis
EPClust (different clustering methods K-means,
Hierarchical Clustering, trasformations
row/columns/both…)
– GO annotation (analysis of gene clusters..)
So How do we start …
• Given a hypothetical sequence predict it
function….
What should we do???
Example
• Amyloids are proteins which tend to
aggregate in solution. Abnormal
accumulation of amyloid in organs is
assumed to play a role in various
neurodegenerative diseases.
Question : can we predict whether a protein
X is an amyolid ?